Overview

Dataset statistics

Number of variables14
Number of observations318344
Missing cells48907
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.0 MiB
Average record size in memory112.0 B

Variable types

Categorical4
DateTime1
Numeric9

Alerts

VERSIE has constant value "1.0"Constant
DATUM_BESTAND has constant value "2022-12-05"Constant
PEILDATUM has constant value "2022-12-01"Constant
TYPERENDE_DIAGNOSE_CD has a high cardinality: 1896 distinct valuesHigh cardinality
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with BEHANDELEND_SPECIALISME_CD and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 48907 (15.4%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.34161442)Skewed

Reproduction

Analysis started2022-12-21 10:29:50.867882
Analysis finished2022-12-21 10:30:08.561565
Duration17.69 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

VERSIE
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1.0
318344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters955032
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 318344
100.0%

Length

2022-12-21T10:30:08.617649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T10:30:08.729985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 318344
100.0%

Most occurring characters

ValueCountFrequency (%)
1 318344
33.3%
. 318344
33.3%
0 318344
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 636688
66.7%
Other Punctuation 318344
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 318344
50.0%
0 318344
50.0%
Other Punctuation
ValueCountFrequency (%)
. 318344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 955032
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 318344
33.3%
. 318344
33.3%
0 318344
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 955032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 318344
33.3%
. 318344
33.3%
0 318344
33.3%

DATUM_BESTAND
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2022-12-05
318344 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3183440
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-05
2nd row2022-12-05
3rd row2022-12-05
4th row2022-12-05
5th row2022-12-05

Common Values

ValueCountFrequency (%)
2022-12-05 318344
100.0%

Length

2022-12-21T10:30:08.819618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T10:30:08.928589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-05 318344
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1273376
40.0%
0 636688
20.0%
- 636688
20.0%
1 318344
 
10.0%
5 318344
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2546752
80.0%
Dash Punctuation 636688
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1273376
50.0%
0 636688
25.0%
1 318344
 
12.5%
5 318344
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 636688
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3183440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1273376
40.0%
0 636688
20.0%
- 636688
20.0%
1 318344
 
10.0%
5 318344
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3183440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1273376
40.0%
0 636688
20.0%
- 636688
20.0%
1 318344
 
10.0%
5 318344
 
10.0%

PEILDATUM
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2022-12-01
318344 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3183440
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-01
2nd row2022-12-01
3rd row2022-12-01
4th row2022-12-01
5th row2022-12-01

Common Values

ValueCountFrequency (%)
2022-12-01 318344
100.0%

Length

2022-12-21T10:30:09.016938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T10:30:09.126151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-01 318344
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1273376
40.0%
0 636688
20.0%
- 636688
20.0%
1 636688
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2546752
80.0%
Dash Punctuation 636688
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1273376
50.0%
0 636688
25.0%
1 636688
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 636688
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3183440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1273376
40.0%
0 636688
20.0%
- 636688
20.0%
1 636688
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3183440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1273376
40.0%
0 636688
20.0%
- 636688
20.0%
1 636688
20.0%

JAAR
Date

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
Minimum2012-01-01 00:00:00
Maximum2022-01-01 00:00:00
2022-12-21T10:30:09.205134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:09.300690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.52447
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-12-21T10:30:09.420465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile335
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation986.55887
Coefficient of variation (CV)2.2548656
Kurtosis61.329571
Mean437.52447
Median Absolute Deviation (MAD)8
Skewness7.9524024
Sum1.3928329 × 108
Variance973298.41
MonotonicityNot monotonic
2022-12-21T10:30:09.542616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 44956
14.1%
313 41298
13.0%
303 36646
11.5%
330 25213
 
7.9%
316 21669
 
6.8%
308 17107
 
5.4%
306 13347
 
4.2%
324 13126
 
4.1%
301 12788
 
4.0%
304 10357
 
3.3%
Other values (18) 81837
25.7%
ValueCountFrequency (%)
301 12788
 
4.0%
302 6977
 
2.2%
303 36646
11.5%
304 10357
 
3.3%
305 44956
14.1%
306 13347
 
4.2%
307 5560
 
1.7%
308 17107
 
5.4%
310 3495
 
1.1%
313 41298
13.0%
ValueCountFrequency (%)
8418 4248
 
1.3%
8416 535
 
0.2%
1900 210
 
0.1%
390 862
 
0.3%
389 3354
 
1.1%
362 4287
 
1.3%
361 2285
 
0.7%
335 3223
 
1.0%
330 25213
7.9%
329 834
 
0.3%
Distinct1896
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
101
 
1345
402
 
1305
403
 
1274
301
 
1273
201
 
1200
Other values (1891)
311947 

Length

Max length4
Median length3
Mean length3.3521882
Min length2

Characters and Unicode

Total characters1067149
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)< 0.1%

Sample

1st row12
2nd row15
3rd row14
4th row07
5th row19

Common Values

ValueCountFrequency (%)
101 1345
 
0.4%
402 1305
 
0.4%
403 1274
 
0.4%
301 1273
 
0.4%
201 1200
 
0.4%
203 1191
 
0.4%
401 1065
 
0.3%
404 1056
 
0.3%
802 1037
 
0.3%
409 1030
 
0.3%
Other values (1886) 306568
96.3%

Length

2022-12-21T10:30:09.683924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
101 1345
 
0.4%
402 1305
 
0.4%
403 1274
 
0.4%
301 1273
 
0.4%
201 1200
 
0.4%
203 1191
 
0.4%
401 1065
 
0.3%
404 1056
 
0.3%
802 1037
 
0.3%
409 1030
 
0.3%
Other values (1886) 306568
96.3%

Most occurring characters

ValueCountFrequency (%)
1 204187
19.1%
0 195580
18.3%
2 141433
13.3%
3 115628
10.8%
5 82324
7.7%
9 76927
 
7.2%
4 75752
 
7.1%
7 62814
 
5.9%
6 55762
 
5.2%
8 45953
 
4.3%
Other values (15) 10789
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1056360
99.0%
Uppercase Letter 10789
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2012
18.6%
M 1824
16.9%
B 1295
12.0%
E 910
8.4%
Z 900
8.3%
D 724
 
6.7%
A 702
 
6.5%
F 671
 
6.2%
C 356
 
3.3%
K 350
 
3.2%
Other values (5) 1045
9.7%
Decimal Number
ValueCountFrequency (%)
1 204187
19.3%
0 195580
18.5%
2 141433
13.4%
3 115628
10.9%
5 82324
7.8%
9 76927
 
7.3%
4 75752
 
7.2%
7 62814
 
5.9%
6 55762
 
5.3%
8 45953
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1056360
99.0%
Latin 10789
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2012
18.6%
M 1824
16.9%
B 1295
12.0%
E 910
8.4%
Z 900
8.3%
D 724
 
6.7%
A 702
 
6.5%
F 671
 
6.2%
C 356
 
3.3%
K 350
 
3.2%
Other values (5) 1045
9.7%
Common
ValueCountFrequency (%)
1 204187
19.3%
0 195580
18.5%
2 141433
13.4%
3 115628
10.9%
5 82324
7.8%
9 76927
 
7.3%
4 75752
 
7.2%
7 62814
 
5.9%
6 55762
 
5.3%
8 45953
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1067149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 204187
19.1%
0 195580
18.3%
2 141433
13.3%
3 115628
10.8%
5 82324
7.7%
9 76927
 
7.2%
4 75752
 
7.1%
7 62814
 
5.9%
6 55762
 
5.2%
8 45953
 
4.3%
Other values (15) 10789
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6010
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4158058 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-12-21T10:30:09.824286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999038
Q199799064
median1.4959903 × 108
Q39.90004 × 108
95-th percentile9.9051604 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9020494 × 108

Descriptive statistics

Standard deviation4.291979 × 108
Coefficient of variation (CV)0.97195827
Kurtosis-1.7411076
Mean4.4158058 × 108
Median Absolute Deviation (MAD)1.1960003 × 108
Skewness0.46371697
Sum1.4057453 × 1014
Variance1.8421084 × 1017
MonotonicityNot monotonic
2022-12-21T10:30:09.975977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2321
 
0.7%
990004007 2284
 
0.7%
990003004 2232
 
0.7%
990004006 1852
 
0.6%
990356076 1688
 
0.5%
990356073 1559
 
0.5%
131999228 1485
 
0.5%
131999164 1469
 
0.5%
990003007 1452
 
0.5%
131999194 1347
 
0.4%
Other values (6000) 300655
94.4%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 11
< 0.1%
10501004 11
< 0.1%
10501005 11
< 0.1%
10501007 3
 
< 0.1%
10501008 11
< 0.1%
10501010 11
< 0.1%
10501011 3
 
< 0.1%
11101002 10
< 0.1%
11101003 11
< 0.1%
ValueCountFrequency (%)
998418081 159
< 0.1%
998418080 143
< 0.1%
998418079 38
 
< 0.1%
998418077 8
 
< 0.1%
998418076 8
 
< 0.1%
998418075 6
 
< 0.1%
998418074 214
0.1%
998418073 214
0.1%
998418072 8
 
< 0.1%
998418071 8
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

Distinct10030
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean513.65484
Minimum1
Maximum165142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-12-21T10:30:10.122039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3103
95-th percentile1743
Maximum165142
Range165141
Interquartile range (IQR)100

Descriptive statistics

Standard deviation3168.7391
Coefficient of variation (CV)6.1690046
Kurtosis406.13596
Mean513.65484
Median Absolute Deviation (MAD)13
Skewness16.6881
Sum1.6351894 × 108
Variance10040907
MonotonicityNot monotonic
2022-12-21T10:30:10.380277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 52487
 
16.5%
2 25668
 
8.1%
3 16746
 
5.3%
4 12323
 
3.9%
5 9605
 
3.0%
6 8140
 
2.6%
7 6746
 
2.1%
8 5785
 
1.8%
9 5208
 
1.6%
10 4686
 
1.5%
Other values (10020) 170950
53.7%
ValueCountFrequency (%)
1 52487
16.5%
2 25668
8.1%
3 16746
 
5.3%
4 12323
 
3.9%
5 9605
 
3.0%
6 8140
 
2.6%
7 6746
 
2.1%
8 5785
 
1.8%
9 5208
 
1.6%
10 4686
 
1.5%
ValueCountFrequency (%)
165142 1
< 0.1%
156428 1
< 0.1%
155884 1
< 0.1%
154269 1
< 0.1%
154183 1
< 0.1%
144724 1
< 0.1%
118395 1
< 0.1%
115938 1
< 0.1%
110520 1
< 0.1%
110215 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct10771
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean606.43279
Minimum1
Maximum239709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-12-21T10:30:10.526993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3113
95-th percentile1989
Maximum239709
Range239708
Interquartile range (IQR)110

Descriptive statistics

Standard deviation4074.3749
Coefficient of variation (CV)6.7185927
Kurtosis725.90388
Mean606.43279
Median Absolute Deviation (MAD)14
Skewness21.341614
Sum1.9305424 × 108
Variance16600531
MonotonicityNot monotonic
2022-12-21T10:30:10.673098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 50560
 
15.9%
2 25222
 
7.9%
3 16585
 
5.2%
4 12115
 
3.8%
5 9513
 
3.0%
6 8130
 
2.6%
7 6712
 
2.1%
8 5724
 
1.8%
9 5150
 
1.6%
10 4687
 
1.5%
Other values (10761) 173946
54.6%
ValueCountFrequency (%)
1 50560
15.9%
2 25222
7.9%
3 16585
 
5.2%
4 12115
 
3.8%
5 9513
 
3.0%
6 8130
 
2.6%
7 6712
 
2.1%
8 5724
 
1.8%
9 5150
 
1.6%
10 4687
 
1.5%
ValueCountFrequency (%)
239709 1
< 0.1%
232359 1
< 0.1%
231983 1
< 0.1%
230952 1
< 0.1%
227936 1
< 0.1%
227457 1
< 0.1%
225066 1
< 0.1%
223938 1
< 0.1%
218449 1
< 0.1%
215070 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

Distinct8978
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7699.6987
Minimum1
Maximum227967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-12-21T10:30:10.813775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q1409
median1726
Q36384
95-th percentile36764
Maximum227967
Range227966
Interquartile range (IQR)5975

Descriptive statistics

Standard deviation17832.563
Coefficient of variation (CV)2.316008
Kurtosis34.326001
Mean7699.6987
Median Absolute Deviation (MAD)1569
Skewness5.0724876
Sum2.4511529 × 109
Variance3.1800032 × 108
MonotonicityNot monotonic
2022-12-21T10:30:10.949332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 526
 
0.2%
8 483
 
0.2%
25 477
 
0.1%
9 465
 
0.1%
19 456
 
0.1%
12 453
 
0.1%
26 451
 
0.1%
22 442
 
0.1%
17 440
 
0.1%
30 439
 
0.1%
Other values (8968) 313712
98.5%
ValueCountFrequency (%)
1 356
0.1%
2 415
0.1%
3 381
0.1%
4 405
0.1%
5 367
0.1%
6 404
0.1%
7 375
0.1%
8 483
0.2%
9 465
0.1%
10 390
0.1%
ValueCountFrequency (%)
227967 23
< 0.1%
223882 23
< 0.1%
217852 24
< 0.1%
214511 17
< 0.1%
213536 25
< 0.1%
211593 17
< 0.1%
210434 19
< 0.1%
205348 17
< 0.1%
200603 16
< 0.1%
198527 20
< 0.1%
Distinct9982
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11077.621
Minimum1
Maximum369837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-12-21T10:30:11.086318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile53
Q1541
median2391
Q39112
95-th percentile51816
Maximum369837
Range369836
Interquartile range (IQR)8571

Descriptive statistics

Standard deviation26570.882
Coefficient of variation (CV)2.3986091
Kurtosis38.096034
Mean11077.621
Median Absolute Deviation (MAD)2192
Skewness5.3301954
Sum3.5264942 × 109
Variance7.0601178 × 108
MonotonicityNot monotonic
2022-12-21T10:30:11.224031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 388
 
0.1%
23 367
 
0.1%
17 364
 
0.1%
52 359
 
0.1%
31 353
 
0.1%
44 352
 
0.1%
40 351
 
0.1%
6 349
 
0.1%
13 346
 
0.1%
5 340
 
0.1%
Other values (9972) 314775
98.9%
ValueCountFrequency (%)
1 283
0.1%
2 318
0.1%
3 321
0.1%
4 303
0.1%
5 340
0.1%
6 349
0.1%
7 325
0.1%
8 289
0.1%
9 269
0.1%
10 323
0.1%
ValueCountFrequency (%)
369837 23
< 0.1%
350599 23
< 0.1%
348523 25
< 0.1%
343080 24
< 0.1%
341692 19
< 0.1%
323791 20
< 0.1%
315780 17
< 0.1%
310778 17
< 0.1%
298646 17
< 0.1%
289045 16
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

Distinct297
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean671458.98
Minimum1462
Maximum1487642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-12-21T10:30:11.374680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1462
5-th percentile42576
Q1287339
median746969
Q31026611
95-th percentile1340835
Maximum1487642
Range1486180
Interquartile range (IQR)739272

Descriptive statistics

Standard deviation412565.29
Coefficient of variation (CV)0.61443111
Kurtosis-1.1119908
Mean671458.98
Median Absolute Deviation (MAD)314357
Skewness0.013732963
Sum2.1375494 × 1011
Variance1.7021012 × 1011
MonotonicityNot monotonic
2022-12-21T10:30:11.515473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880942 5102
 
1.6%
874120 4354
 
1.4%
843981 4347
 
1.4%
894334 4333
 
1.4%
880497 4273
 
1.3%
897710 4212
 
1.3%
764831 4088
 
1.3%
782530 3999
 
1.3%
1081437 3890
 
1.2%
1100537 3866
 
1.2%
Other values (287) 275880
86.7%
ValueCountFrequency (%)
1462 117
 
< 0.1%
1610 130
 
< 0.1%
1702 138
 
< 0.1%
1920 131
 
< 0.1%
2265 183
 
0.1%
2495 173
 
0.1%
6806 380
0.1%
8770 74
 
< 0.1%
13002 371
0.1%
13151 461
0.1%
ValueCountFrequency (%)
1487642 2975
0.9%
1450406 3048
1.0%
1421743 3564
1.1%
1344553 3543
1.1%
1340835 3441
1.1%
1332463 3545
1.1%
1316670 3463
1.1%
1282963 3576
1.1%
1265249 1177
 
0.4%
1262543 1201
 
0.4%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

Distinct296
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1081819.8
Minimum1665
Maximum2666159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-12-21T10:30:11.664424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1665
5-th percentile46598
Q1435553
median1066242
Q31747056
95-th percentile2550054
Maximum2666159
Range2664494
Interquartile range (IQR)1311503

Descriptive statistics

Standard deviation739036.47
Coefficient of variation (CV)0.68314192
Kurtosis-0.78515633
Mean1081819.8
Median Absolute Deviation (MAD)661913
Skewness0.37471763
Sum3.4439085 × 1011
Variance5.461749 × 1011
MonotonicityNot monotonic
2022-12-21T10:30:11.811397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211792 5102
 
1.6%
1281522 4354
 
1.4%
1216262 4347
 
1.4%
1315601 4333
 
1.4%
1300471 4273
 
1.3%
1341874 4212
 
1.3%
1155373 4088
 
1.3%
1167482 3999
 
1.3%
2550054 3890
 
1.2%
2666159 3866
 
1.2%
Other values (286) 275880
86.7%
ValueCountFrequency (%)
1665 117
 
< 0.1%
1861 130
 
< 0.1%
1962 138
 
< 0.1%
2195 131
 
< 0.1%
2816 173
 
0.1%
3015 183
 
0.1%
7385 380
0.1%
9336 74
 
< 0.1%
14580 371
0.1%
14976 461
0.1%
ValueCountFrequency (%)
2666159 3866
1.2%
2620452 3787
1.2%
2595374 3844
1.2%
2588468 3790
1.2%
2550054 3890
1.2%
2481772 3851
1.2%
2179576 3757
1.2%
2062293 3810
1.2%
2052295 1168
 
0.4%
1990240 1167
 
0.4%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

Distinct3517
Distinct (%)1.3%
Missing48907
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean3559.3752
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-12-21T10:30:11.956483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1475
median1245
Q34145
95-th percentile13455
Maximum287220
Range287150
Interquartile range (IQR)3670

Descriptive statistics

Standard deviation6514.5164
Coefficient of variation (CV)1.8302416
Kurtosis148.6648
Mean3559.3752
Median Absolute Deviation (MAD)1015
Skewness7.2625721
Sum9.5902736 × 108
Variance42438923
MonotonicityNot monotonic
2022-12-21T10:30:12.090192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2019
 
0.6%
105 1920
 
0.6%
110 1790
 
0.6%
180 1572
 
0.5%
185 1486
 
0.5%
300 1379
 
0.4%
175 1373
 
0.4%
120 1364
 
0.4%
145 1359
 
0.4%
125 1243
 
0.4%
Other values (3507) 253932
79.8%
(Missing) 48907
 
15.4%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 672
 
0.2%
95 713
 
0.2%
100 922
0.3%
105 1920
0.6%
110 1790
0.6%
115 1046
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
96880 5
< 0.1%

Interactions

2022-12-21T10:30:05.988429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:55.334280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:56.687665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:58.079664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:59.367446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:00.643015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:01.900627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:03.244024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:04.692807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:06.144139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:55.494894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:56.838696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:58.232698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:59.517807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:00.793697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:02.058993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:03.400968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:04.844767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:06.286177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:55.642055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:56.977183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:58.372093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:59.659022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:00.930828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:02.206919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:03.546656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:04.985376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:06.430168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:55.791019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:57.120183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:58.513467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:59.798443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:01.070139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:02.357047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:03.693630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:05.127712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:06.569315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:55.936781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:57.364058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:58.650418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:59.935154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:01.204143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:02.500809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:03.955661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:05.268388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:06.705650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:56.077040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:57.496867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:58.785130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:00.066299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:01.331695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:02.639215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:04.093950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:05.403092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:06.854110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:56.232494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:57.645809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:58.932679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:00.213546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:01.476686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:02.789762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:04.246434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:05.557199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:07.007635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:56.388271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:57.794798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:59.082596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:00.360303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:01.623383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:02.946037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:04.397849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:05.705593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:07.150440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:56.536206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:57.938377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:29:59.223451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:00.501442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:01.763079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:03.093060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:04.542886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-21T10:30:05.845418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-21T10:30:12.211821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
BEHANDELEND_SPECIALISME_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
BEHANDELEND_SPECIALISME_CD1.0000.2180.0080.013-0.060-0.054-0.547-0.4670.049
ZORGPRODUCT_CD0.2181.000-0.141-0.150-0.179-0.211-0.379-0.4040.025
AANTAL_PAT_PER_ZPD0.008-0.1411.0000.9960.3240.3220.0750.084-0.301
AANTAL_SUBTRAJECT_PER_ZPD0.013-0.1500.9961.0000.3210.3230.0780.090-0.304
AANTAL_PAT_PER_DIAG-0.060-0.1790.3240.3211.0000.9880.3220.3040.026
AANTAL_SUBTRAJECT_PER_DIAG-0.054-0.2110.3220.3230.9881.0000.3380.3350.035
AANTAL_PAT_PER_SPC-0.547-0.3790.0750.0780.3220.3381.0000.964-0.012
AANTAL_SUBTRAJECT_PER_SPC-0.467-0.4040.0840.0900.3040.3350.9641.000-0.015
GEMIDDELDE_VERKOOPPRIJS0.0490.025-0.301-0.3040.0260.035-0.012-0.0151.000

Missing values

2022-12-21T10:30:07.440187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-21T10:30:07.967097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02022-12-052022-12-012018-01-013291299002901147501141202198124169545.0
11.02022-12-052022-12-012018-01-01329159900290122652721029107521981241691040.0
21.02022-12-052022-12-012018-01-01329149900290121919979721981241691040.0
31.02022-12-052022-12-012018-01-0132907990029011676690143915002198124169545.0
41.02022-12-052022-12-012018-01-013291959899067111215228521981241691440.0
51.02022-12-052022-12-012018-01-013290299002901113751386487249772198124169545.0
61.02022-12-052022-12-012018-01-01329059900290102562611285134621981241691345.0
71.02022-12-052022-12-012018-01-0132913990029002661561572198124169205.0
81.02022-12-052022-12-012018-01-01329059900290123483561285134621981241691040.0
91.02022-12-052022-12-012018-01-0132919598990651021311522852198124169NaN
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3183341.02022-12-052022-12-012012-01-013032861992990671143164382148764219394832185.0
3183351.02022-12-052022-12-012012-01-0130311311959900711149071611814876421939483NaN
3183361.02022-12-052022-12-012014-01-013138392011003011449185710376122062293NaN
3183371.02022-12-052022-12-012022-01-01306025201100111122242630623155322770.0
3183381.02022-12-052022-12-012014-01-0131372213199906311160204103761220622931635.0
3183391.02022-12-052022-12-012016-01-01303123990356056111212014709133246318320526140.0
3183401.02022-12-052022-12-012022-01-013060381499990072280328920263062315532NaN
3183411.02022-12-052022-12-012022-01-01306086179799017111823263062315532NaN
3183421.02022-12-052022-12-012022-01-0130603020110077112304328125263062315532NaN
3183431.02022-12-052022-12-012016-01-013032981319991761113981891133246318320522945.0